Robustness issues in multilevel regression analysis

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub-units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in ge...

Multilevel regression mixture analysis

A two-level regression mixture model is discussed and contrasted with the conventional two-level regression model. Simulated and real data shed light on the modelling alternatives. The real data analyses investigate gender differences in mathematics achievement from the US National Education Longitudinal Survey.The two-level regression mixture analyses show that unobserved heterogeneity should ...

A Regression Model for Multilevel Homogeneity Analysis

One of the basic techniques that analyzes categorical data is homogeneity analysis. The technique originated in the work of Guttman [17] as a method of scale construction using reciprocal averaging. Burt [5] described homogeneity analysis as a principal components analysis of qualitative data. Hayashi [19] stressed homogeneity analysis as one possible way of quantifying categories. It should be...

Robustness Issues in

Robustness of Sparse Regression Models in fMRI Data Analysis

The primary goal of fMRI analysis is the identification of brain locations, or image voxels, associated with cognitive tasks of interest. Predictive modeling techniques have become widely used in such analysis and sparse modeling techniques have become especially attractive due to their ability to identify relevant locations in a multivariate manner. However, many sparse methods may be too cons...